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To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most suitable define for prediction work. This paper aims to create a generative surrogate model that can be applied on multi-objective optimization problems.

The latest backbone in the field of computer vision (Swin-Transformer, 2021) was introduced and improved as the surrogate function for prediction of the multi-physics field distribution (film cooling effectiveness, pressure, density and velocity). The basic samples were generated by Latin hypercube sampling method and the numerical method adopt for the calculation was validated experimentally at first. The training and testing samples were calculated at experimental conditions. At last, the surrogate model predicted results were verified by experiment in a linear cascade.

The results indicated that comparing with the Multi-Scale Pix2Pix Model, the Swin-Transformer U-Net model presented higher accuracy and computing speed on the prediction of contour results. The computation time for each step of the Swin-Transformer U-Net model is one-third of the original model, especially in the case of multi-physics field prediction. The correlation index reached more than 99.2% and the first-order error was lower than 0.3% for multi-physics field. The predictions of the data-driven surrogate model are consistent with the predictions of the computational fluid dynamics results, and both are very close to the experimental results. The application of the Swin-Transformer model on enlarging the different structure samples will reduce the cost of numerical calculations as well as experiments.

The number of U-Net layers and sample scales has a proper relationship according to equation (8). Too many layers of U-Net will lead to unnecessary nonlinear variation, whereas too few layers will lead to insufficient feature extraction. In the case of Swin-Transformer U-Net model, incorrect number of U-Net layer will reduce the prediction accuracy. The multi-scale Pix2Pix model owns higher accuracy in predicting a single physical field, but the calculation speed is too slow. The Swin-Transformer model is fast in prediction and training (nearly three times faster than multi Pix2Pix model), but the predicted contours have more noise. The neural network predicted results and numerical calculations are consistent with the experimental distribution.

This paper creates a generative surrogate model that can be applied on multi-objective optimization problems. The generative adversarial networks using new backbone is chosen to adjust the output from single contour to multi-physics fields, which will generate more results simultaneously than traditional surrogate models and reduce the time-cost. And it is more applicable to multi-objective spatial optimization algorithms. The Swin-Transformer surrogate model is three times faster to computation speed than the Multi Pix2Pix model. In the prediction results of multi-physics fields, the prediction results of the Swin-Transformer model are more accurate.

Diesel has traditionally been considered the best-suited and most widely used fuel in various sectors, including manufacturing industries, power production, automobiles and transportation. However, with the ongoing crisis of fossil fuel inadequacy, the search for alternative fuels and their application in these sectors has become increasingly important. One particularly interesting and beneficial alternative fuel is biodiesel derived from bio sources.

In this research, an attempt was made to use biodiesel in an unconventional micro gas turbine engine. It will remove the concentric use of diesel engines for power production by improving fuel efficiency as well as increasing the power production rate. Before the fuel is used enormously, it has to be checked in many ways such as performance, emission and combustion analysis experimentally.

In this paper, a detailed experimental study was made for the use of Spirulina microalgae biodiesel in a micro gas turbine. A small-scale setup with the primary micro gas turbine and secondary instruments such as a data acquisition system and AVL gas analyser. The reason for selecting the third-generation microalgae is due to its high lipid and biodiesel production rate. For the conduction of experimental tests, certain conditions were followed in addition that the engine rotating rpm was varied from 4,000, 5,000 and 6,000 rpm. The favourable and predicted results were obtained with the use of microalgae biodiesel.

The performance and combustion results were not exactly equal or greater for biodiesel blends but close to the values of pure diesel; however, the reduction in the emission of CO was at the appreciable level for the used spirulina microalgae biodiesel. The emission of nitrogen oxides and carbon dioxide was a little higher than the use of pure diesel. This experimental analysis results proved that the use of spirulina microalgae biodiesel is both economical and effective replacement for fossil fuel.

The research focused on analysing a unique type of heat exchanger that uses swirling air flow over heated tubes. This heat exchanger includes a round baffle plate with holes and opposite-oriented trapezoidal air deflectors attached at different angles. The deflectors are spaced at various distances, and the tubes are arranged in a circular pattern while maintaining a constant heat flux.

This setup is housed inside a circular duct with airflow in the longitudinal direction. The study examined the impact of different inclination angles and pitch ratios on the performance of the heat exchanger within a specific range of Reynolds numbers.

The findings revealed that the angle of inclination significantly affected the flow velocity, with higher angles resulting in increased velocity. The heat transfer performance was best at lower inclination angles and pitch ratios. Flow resistance decreased with increasing angle of inclination and pitch ratio.

The average thermal enhancement factor decreased with higher inclination angles, with the maximum value observed as 0.94 at a pitch ratio of 1 at an angle of 30°.

Temperature anomalies in the upper troposphere have become a reality as a result of global warming, which has a noticeable impact on aircraft performance. The purpose of this study is to investigate the total air temperature (TAT) anomaly observed during the cruise level and its impact on engine parameter variations.

Empirical methodology is used in this study, and it is based on measurements and observations of anomalous phenomena on the tropopause. The primary data were taken from the Boeing 747-8F's enhanced flight data recorder, which refers to the quantitative method, while the qualitative method is based on a literature review and interviews. The GEnx Integrated Vehicle Health Management system was used for the study's evaluation of engine performance to support the complete range of operational priorities throughout the entire engine lifecycle.

The study's findings indicate that TAT and SAT anomalies, which occur between 270- and 320-feet flight level, have a substantial impact on aircraft performance at cruise altitude and, as a result, on engine parameters, specifically an increase in fuel consumption and engine exhaust gas temperature values. The TAT and Ram Rise anomalies were the focus of the atmospheric deviations, which were assessed as major departures from the International Civil Aviation Organizations–defined International Standard Atmosphere, which is obvious on a positive tendency and so goes against the norms.

Necessary fixed flight parameters gathered from the aircraft's enhanced airborne flight recorder (EAFR) via Aeronautical Radio Incorporated (ARINC) 664 Part 7 at a certain velocity and altitude interfacing with the diagnostic program direct parameter display (DPD), allow for analysis of aircraft performance in a real-time frame. Thus, processed data transmits to the ground maintenance infrastructure for future evaluation and for proper maintenance solutions.

A real-time analysis of aircraft performance is possible using the diagnostic program DPD in conjunction with necessary fixed flight parameters obtained from the aircraft's EAFR via ARINC 664 Part 7 at a specific speed and altitude. Thus, processed data is transmitted to the ground infrastructure for maintenance to be evaluated in the future and to find the best maintenance fixes.

This paper aims to present the application of a tailored systematic engineering design procedure to the concept design of a small production plant for compostable packaging made by straw fibres and bioplastic. In particular, the obtained boxes are intended to be used for wine bottles.

A systematic procedure has been adopted, which underpins on a comprehensive analysis of the design requirements and the function modelling of the process. By considering well-known models of the engineering design process, the work focuses on the early design stages that precede the embodiment design of the whole components of the plant.

The followed design approach allowed to preliminarily evaluate different alternatives of the process from a functional point of view, thus allowing to identify the preferred conceptual process solution. Based on the identified functional sequence, a first evaluation of the potential productivity and the required human resources has been performed.

The procedure shown in this work has been applied only for the considered case of compostable packaging, and other applications are needed to optimize it. Nevertheless, the adopted systematic approach can be adapted for any context where it is necessary to conceive a new production plant for artefacts made by innovative materials.

The work presented in this paper represents one of the few practical examples available in the literature where systematic conceptual design procedures are presented. More specifically, to the best of the authors’ knowledge, this is the very first application of systematic design methods to compostable packaging production.